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About Machine Learning in Compiler Optimisation: 1. Feature Engineering
Machine learning can be applied in compiler optimization in the following ways: 1. Feature engineering extracts static, dynamic, reaction-based, and automatically generated features from code to train models. Dimensionality reduction techniques like PCA and autoencoders compress features. 2. Supervised models like regression, classification, and deep neural networks predict optimization outcomes from features. Unsupervised models like clustering and PCA analyze profiles and reduce dimensions. Reinforcement learning explores large search spaces. 3. Learned models are inserted into compilers to predict optimizations for new programs. 4. Research optimizes sequential and parallel programs, determining transformations, flags, and hardware mappings for performance and energy efficiency. Large datasets are needed to build powerful models.
Detailed Information
| Author: | ['preetham -'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 3 |
| Language: | English |
| Format: | |
| Price: | FREE |
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